Skip to main content
Log in

An implicit opinion analysis model based on feature-based implicit opinion patterns

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

With the rapid growth of social networks, mining customer opinions based on online reviews is crucial to understand consumer needs. Due to the richness of language expressions, customer opinions are often expressed implicitly. However, previous studies usually focus on mining explicit opinions to understand consumer needs. In this paper, we propose a novel implicit opinion analysis model to perform implicit opinion analysis of Chinese customer reviews at both the feature and review levels. First, we extract an implicit-opinionated review/clause dataset from raw review dataset and introduce the concept of the feature-based implicit opinion pattern (FBIOP). Secondly, we develop a clustering algorithm to construct product feature categories. Based on the constructed feature categories, FBIOPs can be mined from the extracted implicit-opinionated clause dataset. Thirdly, the sentiment intensity and polarity of each FBIOP are calculated by using the Chi squared test and pointwise mutual information. Fourthly, according to the resulting FBIOP polarities, the polarities of implicit opinions can be determined at both the feature and review levels. Car forum reviews written in Chinese are collected and labeled as the experimental dataset. The results show that the proposed model outperforms the traditional support vector machine model and the cutting-edge convolutional neural network model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://nlp.stanford.edu/software/lex-parser.shtml.

  2. http://ictclas.nlpir.org.

  3. http://www.keenage.com/html/c_index.html.

  4. https://rdrr.io/rforge/tmcn/man/NTUSD.html.

References

  • Abrahams AS, Fan W, Wang GA et al (2015) An integrated text analytic framework for product defect discovery. Prod Oper Manag 24(6):975–990

    Article  Google Scholar 

  • Amiri H, Zha ZJ, Chua TS (2013) A pattern matching based model for Implicit Opinion Question identification. In: Twenty-seventh AAAI conference on artificial intelligence, pp 46–52

  • Bravo-Marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl-Based Syst 69:86–99

    Article  Google Scholar 

  • Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31:102–107

    Article  Google Scholar 

  • Chen H-Y, Chen H-H (2016) Implicit polarity and implicit aspect recognition in opinion mining. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers). Association for computational linguistics, Berlin, Germany, pp 20–25

  • Chenlo JM, Losada DE (2014) An empirical study of sentence features for subjectivity and polarity classification. Inf Sci 280:275–288

    Article  Google Scholar 

  • Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354

    Article  Google Scholar 

  • Choi Y, Wiebe J, Mihalcea R (2017) Coarse-grained ± effect word sense disambiguation for implicit sentiment analysis. IEEE Trans Affect Comput 8(4):471–479

    Article  Google Scholar 

  • Clarke CE, Bugden D, Hart PS et al (2016) How geographic distance and political ideology interact to influence public perception of unconventional oil/natural gas development. Energy Policy 97:301–309

    Article  Google Scholar 

  • Deng L, Wiebe J (2014) Sentiment propagation via implicature constraints. In: Proceedings of the 14th conference of the european chapter of the association for computational linguistics. Association for computational linguistics, Gothenburg, Sweden, pp 377–385

  • Deng L, Choi Y, Wiebe J (2013) Benefactive/malefactive event and writer attitude annotation. In: Proceedings of the 51st annual meeting of the association for computational linguistics (volume 2: short papers), pp 120–125

  • Feng S, Kang JS, Kuznetsova P, Choi Y (2013) Connotation lexicon: a dash of sentiment beneath the surface meaning. In: Proceedings of the 51st annual meeting of the association for computational linguistics (volume 1: long papers), pp 1774–1784

  • Greene S, Resnik P (2009) More than words: syntactic packaging and implicit sentiment. In: Proceedings of human language technologies: the 2009 annual conference of the North American chapter of the association for computational linguistics on—NAACL’09. Association for computational linguistics, Boulder, Colorado, pp 503–511

  • Hai Z, Chang K, Kim J, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 26(3):623–634

    Article  Google Scholar 

  • Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’04. ACM Press, Seattle, WA, USA, pp 168–177

  • Huang HH, Wang JJ, Chen HH (2017) Implicit opinion analysis: extraction and polarity labelling. J Assoc Inf Sci Technol 68(9):2076–2087

    Article  Google Scholar 

  • Jian P, Jiawei H, Mortazavi-Asl B, et al (2001) PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings 17th international conference on data engineering. IEEE Computer Society, Heidelberg, Germany, pp 215–224

  • Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32:241–254

    Article  Google Scholar 

  • Kang M, Ahn J, Lee K (2018) Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst Appl 94:218–227

    Article  Google Scholar 

  • Kantor P (2001) Foundations of statistical natural language processing. Inf Retr 4:80–81

    Article  Google Scholar 

  • Kenter T, Borisov A, de Rijke M (2016) Siamese CBOW: optimizing word embeddings for sentence representations. arXiv:160604640 [cs]

  • Ku L-W, Chen H-H (2007) Mining opinions from the Web: beyond relevance retrieval. J Am Soc Inform Sci Technol 58(12):1838–1850

    Article  Google Scholar 

  • Kumar S, Yadava M, Roy PP (2019) Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Inf Fusion 52:41–52

    Article  Google Scholar 

  • Lau RYK, Xia Y, Ye Y (2014) A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput Intell Mag 9:31–43

    Article  Google Scholar 

  • Lee SYM (2015) A Linguistic Analysis of Implicit Emotions. In: Lu Q, Gao HH (eds) Chinese lexical semantics. Springer International Publishing, Cham, pp 185–194

    Chapter  Google Scholar 

  • Li X, Lam W (2017) Deep multi-task learning for aspect term extraction with memory interaction. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 2886–2892

  • Li Y, Yang T (2018) Word embedding for understanding natural language: a survey. In: Srinivasan S (ed) Guide to big data applications. Springer International Publishing, Cham, pp 83–104

    Chapter  Google Scholar 

  • Li Y, Pan Q, Yang T et al (2017) Learning word representations for sentiment analysis. Cogn Comput 9:843–851

    Article  Google Scholar 

  • Lin C, He Y, Everson R, Ruger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24(6):1134–1145

    Article  Google Scholar 

  • Liu X, Zhou M (2011) Sentence-level sentiment analysis via sequence modeling. In: Zhang J (ed) Applied informatics and communication. Springer, Berlin, pp 337–343

    Chapter  Google Scholar 

  • Liu B, Stede M, Tiedemann J et al (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5:1–167

    Article  Google Scholar 

  • Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Thirty-second AAAI conference on artificial intelligence, pp 5876–5883

  • Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. In: Ho TB, Cheung D, Liu H (eds) Advances in knowledge discovery and data mining. Springer, Berlin, pp 301–311

    Chapter  Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing—EMNLP’02. Association for computational linguistics, not known, pp 79–86

  • Peng H, Cambria E, Hussain A (2017) A review of sentiment analysis research in chinese language. Cogn Comput 9:423–435

    Article  Google Scholar 

  • Poria S, Cambria E, Gelbukh A et al (2015) Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput Intell Mag 10:26–36

    Article  Google Scholar 

  • Poria S, Cambria E, Gelbukh A (2016a) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49

    Article  Google Scholar 

  • Poria S, Chaturvedi I, Cambria E, Bisio F (2016b) Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In: 2016 International joint conference on neural networks (IJCNN). pp 4465–4473

  • Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46

    Article  Google Scholar 

  • Scheffé H (1947) The relation of control charts to analysis of variance and chi square tests. J Am Stat Assoc 42:425–431

    Article  MathSciNet  Google Scholar 

  • Schouten K, Frasincar F (2016) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830

    Article  Google Scholar 

  • Senecal S, Nantel J (2004) The influence of online product recommendations on consumers’ online choices. J Retail 80:159–169

    Article  Google Scholar 

  • Tang D, Wei F, Qin B et al (2016) Sentiment embeddings with applications to sentiment analysis. IEEE Trans Knowl Data Eng 28:496–509

    Article  Google Scholar 

  • Toprak C, Jakob N, Gurevych I (2010) Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for computational linguistics, Stroudsburg, PA, USA, pp 575–584

  • Turney PD (2001) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics—ACL’02. Association for computational linguistics, Philadelphia, Pennsylvania, pp 417–424

  • Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing. Association for computational linguistics, Austin, Texas, pp 606–615

  • Wei C-P, Chen Y-M, Yang C-S, Yang CC (2010) Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Inf Syst E-Bus Manag 8:149–167

    Article  Google Scholar 

  • Wilson T (2008) Annotating subjective content in meetings. In: LREC, pp 2738–2745

  • Xia R, Zong C (2010) Exploring the use of word relation features for sentiment classification. In: International conference on computational linguistics: posters, pp 1336–1344

  • Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181:1138–1152

    Article  Google Scholar 

  • Xianghua F, Guo L, Yanyan G, Zhiqiang W (2013) Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl Based Syst 37:186–195

    Article  Google Scholar 

  • Yan Z, Xing M, Zhang D, Ma B (2015) EXPRS: an extended pagerank method for product feature extraction from online consumer reviews. Inf Manag 52:850–858

    Article  Google Scholar 

  • Yu L-C, Wang J, Lai KR, Zhang X (2017) Refining word embeddings for sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing. Association for computational linguistics, Copenhagen, Denmark, pp 534–539

  • Zhai Z, Liu B, Xu H, Jia P (2011) Constrained LDA for grouping product features in opinion mining. In: Huang JZ, Cao L, Srivastava J (eds) Advances in knowledge discovery and data mining. Springer, Berlin, pp 448–459

    Chapter  Google Scholar 

  • Zhang L, Liu B (2011) Identifying noun product features that imply opinions. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: short papers—volume 2. Association for computational linguistics, Stroudsburg, PA, USA, pp 575–580

  • Zhang W, Li C, Ye Y et al (2015) Dynamic business network analysis for correlated stock price movement prediction. IEEE Intell Syst 30:26–33

    Article  Google Scholar 

  • Zhang Y, Chen M, Huang D et al (2017) iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener Comput Syst 66:30–35

    Article  Google Scholar 

  • Zhao W, Guan Z, Chen L et al (2018) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans Knowl Data Eng 30(1):185–197

    Article  Google Scholar 

  • Zheng L, Wang H, Gao S (2018) Sentimental feature selection for sentiment analysis of Chinese online reviews. Int J Mach Learn Cybern 9:75–84

    Article  Google Scholar 

  • Zuo Y, Wu J, Zhang H et al (2018) Complementary aspect-based opinion mining. IEEE Trans Knowl Data Eng 30(2):249–262

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by Grants from the National Natural Science Foundation of China (Nos. 71501055, 71690230).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, Z., Zhang, Q., Tang, X. et al. An implicit opinion analysis model based on feature-based implicit opinion patterns. Artif Intell Rev 53, 4547–4574 (2020). https://doi.org/10.1007/s10462-019-09801-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09801-9

Keywords

Navigation